# Developing Models to Identify Veterans with Nonalcoholic Fatty Liver Disease and Predict Progression

> **NIH VA I21** · RALPH H JOHNSON VA MEDICAL CENTER · 2020 · —

## Abstract

Anticipated Impacts on Veterans Health Care: This proposal will use natural language processing (NLP)
methods and machine learning approaches to provide and compare predictive models of non-alcoholic fatty
liver disease (NAFLD) among Veterans. Proposed analyses will also examine racial/ethnic differences in
NAFLD diagnosis, treatment, and outcomes with the goal of identify patient groups at highest risk of
progression to liver cirrhosis and cirrhosis-related complications. The long-term goal of this research, which
this pilot study will facilitate, is the development and effective targeting of integrated multidisciplinary
treatment algorithms alongside simple, culturally appropriate, and cost-effective interventions to curb the
epidemic of NAFLD and its complications among Veterans.
Background: NAFLD is a significant and growing health problem closely associated with obesity, type 2
diabetes mellitus (T2DM), hypertension, and dyslipidemia. In the VA, NAFLD prevalence has been
estimated as high as 46%. The prevalence of NAFLD varies significantly depending on the population
studied and on the tests used. In the Dallas Heart Study, it was estimated that over 30% of patients had
NAFLD by MR spectroscopy. Importantly, investigators found that the highest prevalence of NAFLD
occurred among Hispanics (58%), and those with T2DM (over 70%). Hispanic populations have higher
incidence of NAFLD and potentially higher rates of progression to advanced fibrosis, compared to non-
Hispanic White (NHW) patients. Current therapy aims to optimize both cardiovascular and liver-related risk
factors (i.e. T2DM, hypertension, hyperlipidemia, obesity, smoking etc.). Lifestyle changes driven by dietary
intervention and exercise are the first line of therapy to induce and maintain weight loss, reducing fat mass,
hyperinsulinemia and insulin resistance, thus decreasing lipotoxic liver damage and multisystem metabolic
consequences. The VA NAFLD Clinic provides Intensive Weight Loss that includes nutrition, exercise,
behavioral, VA approved pharmaceuticals (e.g., Bupropion/Naltrex, Lorcascerin) and bariatric surgery.
Hence it is important to identify patients that are at high risk of progression to the poor outcomes associated
with advanced NAFLD and provide treatments available at VA NAFLD Clinics.
Objectives: In this 1-year pilot, we propose using the VA NAFLD Team curated cohort (n=61,900) of
Veterans from the national Veteran Affairs Informatics and Computing Infrastructure (VINCI) system who
have received liver biopsies. The dataset will be augmented to include medical records 8-years prior and 1-
year post biopsy. We will use clustering and machine learning predictive analytic approaches to identify
patients with higher risk of developing cirrhosis, cirrhosis-related complications, and cardiovascular events
with a focused analysis on racial and ethnicity disparities.
Methods: The machine learning methodology of convolutional neural networks and random forests will be
u...

## Key facts

- **NIH application ID:** 10177897
- **Project number:** 5I21HX002700-02
- **Recipient organization:** RALPH H JOHNSON VA MEDICAL CENTER
- **Principal Investigator:** Lewis James Frey
- **Activity code:** I21 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2020
- **Award amount:** —
- **Award type:** 5
- **Project period:** 2019-04-01 → 2020-09-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10177897

## Citation

> US National Institutes of Health, RePORTER application 10177897, Developing Models to Identify Veterans with Nonalcoholic Fatty Liver Disease and Predict Progression (5I21HX002700-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10177897. Licensed CC0.

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